Techniques for Data Routing and Management Using Risk Classification and Data Sampling

2. The computer-implemented method of claim 1, further comprising generating one or more of risk level components that are dependent on outcomes associated with other risk level components generated from a plurality of risk classifiers.

3. The computer-implemented method of claim 2, further comprising removing a risk classifier from the plurality of risk classifiers based at least in part on the one or more risk level components.

6. The system of claim 5, wherein the one or more services further remove a risk classifier from a plurality of risk classifiers based at least in part on one or more risk level components.

7. The system of claim 6, wherein the one or more services further generate the one or more risk level components.

8. The system of claim 6, wherein the one or more risk level components are dependent on outcomes associated with other risk level components.

9. The system of claim 8, wherein the other risk level components are generated from the plurality of risk classifiers.

12. The non-transitory computer-readable storage medium of claim 11, wherein the instructions further cause the computer system to generate one or more of risk level components.

13. The non-transitory computer-readable storage medium of claim 12, wherein the instructions further cause the computer system to remove a risk classifier from a plurality of risk classifiers based at least in part on the one or more risk level components.

14. The non-transitory computer-readable storage medium of claim 13, wherein the one or more risk level components are dependent on outcomes associated with other risk level components.

15. The non-transitory computer-readable storage medium of claim 13, wherein the other risk level components are generated from the plurality of risk classifiers.

16. The non-transitory computer-readable storage medium of claim 11, wherein the instructions further cause the computer system to provide an input into the replicated traffic sample using the characteristic so as to generate the observed behavior.

17. The non-transitory computer-readable storage medium of claim 11, wherein the instructions further cause the computer system to train one or more risk classifiers based at least in part on a comparison of the expected behavior and the observed behavior.